Siamese Networks are used for metric learning and were originally developed for verifying signatures using Neural Networks [1]}.
A few years later, they were employed in face verification for the first time, using a new loss function based on Energy-Based Models (EBM), called Contrastive Loss [2]}.
Since the boom of CNNs in face recognition, Siamese Networks have been widely used for face verification in large datasets, obtaining great performance over other methods [3]}, [4]}, [5]}.
As Siamese Networks are a metric learning method, they make use of similarity scores to perform recognition.
Several works have also proposed approaches based on thresholding similarity-like scores, but focusing generally on open-set face recognition [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}.
